Cardiac localization in topograms using hierarchical models

A vast number of medical imaging protocols identify anatomical regions of interest (ROI) from two dimensional (2D) localizer images to aid high resolution scan planning. These localizer scans are typically two dimensional projections of three dimensional data and as such have lower image detail due to overlapping tissue. The problem is further complicated by large variations in shape, size, appearance and the high occurrence of anomalies in the human anatomy. Manual ROI delineation is time consuming and error prone. To combat these issues we develop a hierarchical multi-object active appearance model (AAM) framework that is both robust to inaccuracies in model initialization yet sufficiently flexible to handle the large diversity of the human body. The method was successfully applied to automatically determine the extents of the human heart in 99 2D CT topograms yielding significant improvement in accuracy over a single global AAM approach.